ContentGAN [1]} uses semantic visual information to generate layouts for magazine pages. But as mentioned earlier, due to the lack of spatial information and detailed features, it encounters problems when applied to poster layout generation, such as the occlusion of subjects. In contrast, by mining image-composition information, our method can better understand image contents and output high-quality layouts for posters.
